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Heterogeneous Data Mining for Planning Active Surveillance of Malaria

Published: 07 October 2015 Publication History

Abstract

Malaria is one of the most serious diseases in the world, which is densely distributed in poverty and remote areas. In the prevention and control of malaria, active surveillance is more efficient than passive surveillance to discover the incidences timely and accurately. However, it is always faced with the challenge of how to allocate the limited sources, such as medical staff and medicine, appropriately so as to achieve a maximum infect. In this paper, we propose a novel method to characterize the spatiotemporal patterns of infection risk for active surveillance planning. Specifically, we propose a temporal heterogeneous diffusion network model to discover high risk areas timely, and a mixture optimization method to find high risk areas accurately. The validation on existing real-world data shows that our method outperforms the existing state-of-the-art both in terms of infection risk prediction and planning of active surveillance under different thresholds.

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  • (2023)Surveillance of communicable diseases using social media: A systematic reviewPLOS ONE10.1371/journal.pone.028210118:2(e0282101)Online publication date: 24-Feb-2023

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cover image ACM Other conferences
ASE BD&SI '15: Proceedings of the ASE BigData & SocialInformatics 2015
October 2015
381 pages
ISBN:9781450337359
DOI:10.1145/2818869
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 07 October 2015

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Author Tags

  1. active surveillance
  2. heterogeneous diffusion network
  3. mixture optimization method
  4. spatiotemporal patterns mining

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ASE BD&SI '15
ASE BD&SI '15: ASE BigData & SocialInformatics 2015
October 7 - 9, 2015
Kaohsiung, Taiwan

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View all
  • (2023)Surveillance of communicable diseases using social media: A systematic reviewPLOS ONE10.1371/journal.pone.028210118:2(e0282101)Online publication date: 24-Feb-2023

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